Digital imaging systems and methods of analyzing pixel data of an image of a shaving stroke for determining pressure being applied to a user&#39;s skin

ABSTRACT

Digital imaging systems and methods are described for analyzing pixel data of an image of a shaving stroke for determining pressure being applied to a user&#39;s skin. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective individual when shaving with a shaving razor. A pressure model, trained with the pixel data, is operable to determine pressure being applied to the respective individual&#39;s skin. An image of a user when shaving with the shaving razor is received and analyzed, by the pressure model, to determine a user-specific pressure being applied to the user&#39;s skin by the shaving razor when a user shaving stroke is taken. A user-specific electronic recommendation to address at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

FIELD

The present disclosure generally relates to digital imaging systems andmethods, and more particularly to, digital imaging systems and methodsfor analyzing pixel data of an image of a shaving stroke for determiningpressure being applied to a user's skin.

BACKGROUND

Generally, shave performance can be summarized as a trade-off betweencloseness and irritation, where an individual typically can eitherachieve, on the one hand, an increased closeness of shave (removing morehair) but risking irritation or redness of his or her skin, or, on theother hand, a less close shave (leaving more hair) but reducing the riskof skin irritation. Individuals typically try to balance this trade-offto get their desired end result by manually regulating the quantity,direction and pressure (or load) of strokes applied during a shave.Taking an increased quantity of strokes, taking strokes going againstthe direction of hair growth or applying increased pressure duringstrokes will typically result in both increased closeness and increasedrisk of skin irritation. However, there is typically a threshold valuefor such shave parameters, going beyond this threshold value will yieldminimal increase closeness benefit while yielding a high risk ofunwanted skin irritation.

Thus a problem arises for existing shaving razors, and the use thereof,where individuals desiring a close shave generally apply too manystrokes, too many strokes going against the hair growth direction and/ortoo much pressure (or load) during a shave session, under the falseimpression that it will improve the closeness of the end result. Theproblem is acutely pronounced given the various versions, brands, andtypes of shaving razors currently available to individuals, where eachof the versions, brands, and types of shaving razors have differentcomponents, blades, sharpness, and/or otherwise differentconfigurations, all of which can vary significantly in the quantity,direction and pressure (or load) of strokes required, and for eachshaving razor type, to achieve a close shave (e.g., with little or nohair remaining) with little or no skin irritation. This problem isparticularly acute because such existing shaving razors—which may bedifferently configured—provide little or no feedback or guidance toassist the individual achieve a close shave without skin irritation.

For the foregoing reasons, there is a need for digital imaging systemsand methods for analyzing pixel data of an image of a shaving stroke fordetermining pressure being applied to a user's skin.

SUMMARY

Generally, as described herein, the digital systems and methods foranalyzing pixel data of an image of a shaving stroke for determiningpressure being applied to a user's skin, provide a digital imaging, andartificial intelligence (AI), based solution for overcoming problemsthat arise from incorrect use of different shaving razors. The digitalsystems and methods allow a user to submit a specific user image toimaging server(s) (e.g., including its one or more processors), orotherwise a computing device (e.g., such as locally on the user's mobiledevice), where the imaging server(s) or user computing device implementsor executes a pressure model trained with pixel data of potentially10,000s (or more) images of individuals using a shaving razor. Thepressure model may generate, based on a determined user-specificpressure, a user-specific electronic recommendation designed to addressat least one feature identifiable within the pixel data comprising atleast a portion of the shaving razor applied to the user's skin. Forexample, the at least one feature can comprise pixels or pixel dataindicative of an overage of pressure applied to the user's skin. In someembodiments, the user-specific recommendation (and/or product specificrecommendation) may be transmitted via a computer network to a usercomputing device of the user for rendering on a display screen. In otherembodiments, no transmission to the imaging server of the user'sspecific image occurs, where the user-specific recommendation (and/orproduct specific recommendation) may instead be generated by thepressure model, executing and/or implemented locally on the user'smobile device and rendered, by a processor of the mobile device, on adisplay screen of the mobile device. In various embodiments, suchrendering may include graphical representations, overlays, annotations,and the like for addressing the feature in the pixel data.

More specifically, as describe herein, a digital imaging method ofanalyzing pixel data of an image of a shaving stroke for determiningpressure being applied to a user's skin is disclosed. The digitalimaging method comprises: (a) aggregating, at one or more processorscommunicatively coupled to one or more memories, a plurality of trainingimages of a plurality of individuals, each of the training imagescomprising pixel data of a respective individual when shaving with ashaving razor. The digital imaging method may further comprise: (b)training, by the one or more processors with the pixel data of theplurality of training images, a pressure model operable to determinepressure being applied to the respective individual's skin by a shavingrazor when a shaving stroke is taken. The digital imaging method mayfurther comprise: (c) receiving, at the one or more processors, at leastone image of a user when shaving with the shaving razor, where the atleast one image is captured by a digital camera. The at least one imagemay comprise pixel data of at least a portion of the shaving razorapplied to the user's skin. The digital imaging method may furthercomprise: (d) analyzing, by the pressure model executing on the one ormore processors, the at least one image captured by the digital camerato determine a user-specific pressure being applied to the user's skinby the shaving razor when a user shaving stroke is taken. The digitalimaging method may further comprise: (e) generating, by the one or moreprocessors based on the user-specific pressure, at least oneuser-specific electronic recommendation designed to address at least onefeature identifiable within the pixel data comprising the at least theportion of the shaving razor applied to the user's skin. The digitalimaging method may further comprise: (f) rendering, on a display screenof a user computing device, the at least one user-specificrecommendation.

In addition, as described herein, a digital imaging system is disclosed,configured to analyze pixel data of an image of a shaving stroke fordetermining pressure being applied to a user's skin. The digital imagingsystem may comprise an imaging server comprising a server processor anda server memory. The digital imaging system may further comprise animaging application (app) configured to execute on a user computingdevice comprising a device processor and a device memory. The imagingapp may be communicatively coupled to the imaging server. The digitalimaging system may further comprise a pressure model trained with pixeldata of a plurality of training images of individuals and operable todetermine pressure being applied to a respective individual's skin by ashaving razor when a shaving stroke is taken. The pressure model may beconfigured to execute on the server processor or the device processor tocause the server processor or the device processor to receive at leastone image of a user when shaving with the shaving razor. The at leastone image may be captured by a digital camera. The at least one imagecomprises pixel data of at least a portion of the shaving razor appliedto the user's skin. The pressure model may be further configured toexecute on the server processor or the device processor to cause theserver processor or the device processor to analyze, by the pressuremodel, the at least one image captured by the digital camera todetermine a user-specific pressure being applied to the user's skin bythe shaving razor when a user shaving stroke is taken. The pressuremodel may be further configured to execute on the server processor orthe device processor to cause the server processor or the deviceprocessor to generate, based on the user-specific pressure, at least oneuser-specific electronic recommendation designed to address at least onefeature identifiable within the pixel data comprising the at least theportion of the shaving razor applied to the user's skin. The pressuremodel may be further configured to execute on the server processor orthe device processor to cause the server processor or the deviceprocessor to render, on a display screen of the user computing device ofthe user, the at least one user-specific recommendation.

Further, as described herein, a tangible, non-transitorycomputer-readable medium storing instructions for analyzing pixel dataof an image of a shaving stroke for determining pressure being appliedto a user's skin is disclosed. The instructions, when executed by one ormore processors may cause the one or more processors to: (a) aggregate,at one or more processors communicatively coupled to one or morememories, a plurality of training images of a plurality of individuals,each of the training images comprising pixel data of a respectiveindividual when shaving with a shaving razor; (b) train, by the one ormore processors with the pixel data of the plurality of training images,a pressure model operable to determine pressure being applied to therespective individual's skin by a shaving razor when a shaving stroke istaken; (c) receive, at the one or more processors, at least one image ofa user when shaving with the shaving razor, the at least one imagecaptured by a digital camera, and the at least one image comprisingpixel data of at least a portion of the shaving razor applied to theuser's skin; (d) analyze, by the pressure model executing on the one ormore processors, the at least one image captured by the digital camerato determine a user-specific pressure being applied to the user's skinby the shaving razor when a user shaving stroke is taken; (e) generate,by the one or more processors based on the user-specific pressure, atleast one user-specific electronic recommendation designed to address atleast one feature identifiable within the pixel data comprising the atleast the portion of the shaving razor applied to the user's skin; and(f) render, on a display screen of a user computing device, the at leastone user-specific recommendation.

In accordance with the above, and with the disclosure herein, thepresent disclosure includes improvements in computer functionality or inimprovements to other technologies at least because the disclosuredescribes that, e.g., an imaging server, or otherwise computing device(e.g., a user computer device), is improved where the intelligence orpredictive ability of the imaging server or computing device is enhancedby a trained (e.g., machine learning trained) pressure model. Thepressure model, executing on the imaging server or computing device, isable to accurately identify, based on pixel data of other individuals, auser-specific pressure and a user-specific electronic recommendationdesigned to address at least one feature identifiable within the pixeldata of a specific user comprising the at least the portion of theshaving razor applied to the user's skin. That is, the presentdisclosure describes improvements in the functioning of the computeritself or “any other technology or technical field” because an imagingserver or user computing device is enhanced with a plurality of trainingimages (e.g., 10,000s of training images and related pixel data asfeature data) to accurately predict, detect, or determine pixel data ofa user-specific images, such as newly provided customer images. Thisimproves over the prior art at least because existing systems lack suchpredictive or classification functionality and are simply not capable ofaccurately analyzing user-specific images to output a predictive resultto address at least one feature identifiable within the pixel datacomprising the at least the portion of the shaving razor applied to theuser's skin.

For similar reasons, the present disclosure relates to improvement toother technologies or technical fields at least because the presentdisclosure describes or introduces improvements to computing devices inthe field of shaving razors, whereby the trained pressure modelexecuting on the imaging device(s) or computing devices improve thefield of shaving and/or shaving devices with digital and/or artificialintelligence based analysis of user or individual images to output apredictive result to address user-specific pixel data of at least onefeature identifiable within the pixel data comprising the at least theportion of the shaving razor applied to the user's skin.

In addition, the present disclosure includes applying certain of theclaim elements with, or by use of, a particular machine, e.g., a shavingrazor, which appears in the images used to train the pressure model andfurther appears in the images submitted by a user to determine auser-specific pressure being applied to the user's skin by the shavingrazor when a user shaving stroke is taken.

In addition, the present disclosure includes specific features otherthan what is well-understood, routine, conventional activity in thefield, or adding unconventional steps that confine the claim to aparticular useful application, e.g., analyzing pixel data of an image ofa shaving stroke for determining pressure being applied to a user's skinas described herein.

Advantages will become more apparent to those of ordinary skill in theart from the following description of the preferred embodiments whichhave been shown and described by way of illustration. As will berealized, the present embodiments may be capable of other and differentembodiments, and their details are capable of modification in variousrespects. Accordingly, the drawings and description are to be regardedas illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed system andmethods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1 illustrates an example digital imaging system configured toanalyze pixel data of an image of a shaving stroke for determiningpressure being applied to a user's skin, in accordance with variousembodiments disclosed herein.

FIG. 2A illustrates an example image and its related pixel data that maybe used for training and/or implementing a pressure model, in accordancewith various embodiments disclosed herein.

FIG. 2B illustrates a further example image and its related pixel datathat may be used for training and/or implementing a pressure model, inaccordance with various embodiments disclosed herein.

FIG. 2C illustrates a further example image and its related pixel datathat may be used for training and/or implementing a pressure model, inaccordance with various embodiments disclosed herein.

FIG. 3 illustrates a diagram of a digital imaging method of analyzingpixel data of an image of a shaving stroke for determining pressurebeing applied to a user's skin, in accordance with various embodimentsdisclosed herein.

FIG. 4 illustrates an example user interface as rendered on a displayscreen of a user computing device in accordance with various embodimentsdisclosed herein.

The Figures depict preferred embodiments for purposes of illustrationonly. Alternative embodiments of the systems and methods illustratedherein may be employed without departing from the principles of theinvention described herein.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates an example digital imaging system 100 configured toanalyze pixel data of an image (e.g., any one or more of images 202 a,202 b, and/or 202 c) of a shaving stroke for determining pressure beingapplied to a user's skin or body, in accordance with various embodimentsdisclosed herein. As referred to herein, a “body” may refer to anyportion of the human body including the torso, waist, face, head, arm,leg, or other appendage or portion or part of the body thereof. In theexample embodiment of FIG. 1, digital imaging system 100 includesserver(s) 102, which may comprise one or more computer servers. Invarious embodiments server(s) 102 comprise multiple servers, which maycomprise a multiple, redundant, or replicated servers as part of aserver farm. In still further embodiments, server(s) 102 may beimplemented as cloud-based servers, such as a cloud-based computingplatform. For example, imaging server(s) 102 may be any one or morecloud-based platform(s) such as MICROSOFT AZURE, AMAZON AWS, or thelike. Server(s) 102 may include one or more processor(s) 104 as well asone or more computer memories 106. Server(s) 102 may be referred toherein as “imaging server(s).”

The memories 106 may include one or more forms of volatile and/ornon-volatile, fixed and/or removable memory, such as read-only memory(ROM), electronic programmable read-only memory (EPROM), random accessmemory (RAM), erasable electronic programmable read-only memory(EEPROM), and/or other hard drives, flash memory, MicroSD cards, andothers. The memorie(s) 106 may store an operating system (OS) (e.g.,Microsoft Windows, Linux, UNIX, etc.) capable of facilitating thefunctionalities, apps, methods, or other software as discussed herein.The memorie(s) 106 may also store a pressure model 108, which may be anartificial intelligence based model, such as a machine learning model,trained on various images (e.g., images 202 a, 202 b, and/or 202 c), asdescribed herein. Additionally, or alternatively, the pressure model 108may also be stored in database 105, which is accessible or otherwisecommunicatively coupled to imaging server(s) 102. The memories 106 mayalso store machine readable instructions, including any of one or moreapplication(s), one or more software component(s), and/or one or moreapplication programming interfaces (APIs), which may be implemented tofacilitate or perform the features, functions, or other disclosuredescribed herein, such as any methods, processes, elements orlimitations, as illustrated, depicted, or described for the variousflowcharts, illustrations, diagrams, figures, and/or other disclosureherein. For example, at least some of the applications, softwarecomponents, or APIs may be, include, otherwise be part of, an imagingbased machine learning model or component, such as the pressure model108, where each may be configured to facilitate their variousfunctionalities discussed herein. It should be appreciated that one ormore other applications may be envisioned and that are executed by theprocessor(s) 104.

The processor(s) 104 may be connected to the memories 106 via a computerbus responsible for transmitting electronic data, data packets, orotherwise electronic signals to and from the processor(s) 104 andmemories 106 in order to implement or perform the machine readableinstructions, methods, processes, elements or limitations, asillustrated, depicted, or described for the various flowcharts,illustrations, diagrams, figures, and/or other disclosure herein.

The processor(s) 104 may interface with the memory 106 via the computerbus to execute the operating system (OS). The processor(s) 104 may alsointerface with the memory 106 via the computer bus to create, read,update, delete, or otherwise access or interact with the data stored inthe memories 106 and/or the database 104 (e.g., a relational database,such as Oracle, DB2, MySQL, or a NoSQL based database, such as MongoDB).The data stored in the memories 106 and/or the database 105 may includeall or part of any of the data or information described herein,including, for example, training images and/or user images (e.g., eitherof which including any one or more of images 202 a, 202 b, and/or 202 c)or other information of the user, including demographic, age, race, skintype, or the like.

The imaging server(s) 102 may further include a communication componentconfigured to communicate (e.g., send and receive) data via one or moreexternal/network port(s) to one or more networks or local terminals,such as computer network 120 and/or terminal 109 (for rendering orvisualizing) described herein. In some embodiments, imaging server(s)102 may include a client-server platform technology such as ASP.NET,Java J2EE, Ruby on Rails, Node.js, a web service or online API,responsive for receiving and responding to electronic requests. Theimaging server(s) 102 may implement the client-server platformtechnology that may interact, via the computer bus, with the memories(s)106 (including the applications(s), component(s), API(s), data, etc.stored therein) and/or database 105 to implement or perform the machinereadable instructions, methods, processes, elements or limitations, asillustrated, depicted, or described for the various flowcharts,illustrations, diagrams, figures, and/or other disclosure herein.According to some embodiments, the imaging server(s) 102 may include, orinteract with, one or more transceivers (e.g., WWAN, WLAN, and/or WPANtransceivers) functioning in accordance with IEEE standards, 3GPPstandards, or other standards, and that may be used in receipt andtransmission of data via external/network ports connected to computernetwork 120. In some embodiments, computer network 120 may comprise aprivate network or local area network (LAN). Additionally, oralternatively, computer network 120 may comprise a public network suchas the Internet.

Imaging server(s) 102 may further include or implement an operatorinterface configured to present information to an administrator oroperator and/or receive inputs from the administrator or operator. Asshown in FIG. 1, an operator interface may provide a display screen(e.g., via terminal 109). Imaging server(s) 102 may also provide I/Ocomponents (e.g., ports, capacitive or resistive touch sensitive inputpanels, keys, buttons, lights, LEDs), which may be directly accessiblevia or attached to imaging server(s) 102 or may be indirectly accessiblevia or attached to terminal 109. According to some embodiments, anadministrator or operator may access the server 102 via terminal 109 toreview information, make changes, input training data or images, and/orperform other functions.

As described above herein, in some embodiments, imaging server(s) 102may perform the functionalities as discussed herein as part of a “cloud”network or may otherwise communicate with other hardware or softwarecomponents within the cloud to send, retrieve, or otherwise analyze dataor information described herein.

In general, a computer program or computer based product, application,or code (e.g., the model(s), such as AI models, or other computinginstructions described herein) may be stored on a computer usablestorage medium, or tangible, non-transitory computer-readable medium(e.g., standard random access memory (RAM), an optical disc, a universalserial bus (USB) drive, or the like) having such computer-readableprogram code or computer instructions embodied therein, wherein thecomputer-readable program code or computer instructions may be installedon or otherwise adapted to be executed by the processor(s) 104 (e.g.,working in connection with the respective operating system in memories106) to facilitate, implement, or perform the machine readableinstructions, methods, processes, elements or limitations, asillustrated, depicted, or described for the various flowcharts,illustrations, diagrams, figures, and/or other disclosure herein. Inthis regard, the program code may be implemented in any desired programlanguage, and may be implemented as machine code, assembly code, bytecode, interpretable source code or the like (e.g., via Golang, Python,C, C++, C#, Objective-C, Java, Scala, ActionScript, JavaScript, HTML,CSS, XML, etc.).

As shown in FIG. 1, imaging server(s) 102 are communicatively connected,via computer network 120 to the one or more user computing devices 111 c1-111 c 3 and/or 112 c 1-112 c 3 via base stations 111 b and 112 b. Insome embodiments, base stations 111 b and 112 b may comprise cellularbase stations, such as cell towers, communicating to the one or moreuser computing devices 111 c 1-111 c 3 and 112 c 1-112 c 3 via wirelesscommunications 121 based on any one or more of various mobile phonestandards, including NMT, GSM, CDMA, UMMTS, LTE, 5G, or the like.Additionally or alternatively, base stations 111 b and 112 b maycomprise routers, wireless switches, or other such wireless connectionpoints communicating to the one or more user computing devices 111 c1-111 c 3 and 112 c 1-112 c 3 via wireless communications 122 based onany one or more of various wireless standards, including by non-limitingexample, IEEE 802.11a/b/c/g (WIFI), the BLUETOOTH standard, or the like.

Any of the one or more user computing devices 111 c 1-111 c 3 and/or 112c 1-112 c 3 may comprise mobile devices and/or client devices foraccessing and/or communications with imaging server(s) 102. In variousembodiments, user computing devices 111 c 1-111 c 3 and/or 112 c 1-112 c3 may comprise a cellular phone, a mobile phone, a tablet device, apersonal data assistance (PDA), or the like, including, by non-limitingexample, an APPLE iPhone or iPad device or a GOOGLE ANDROID based mobilephone or table. In still further embodiments, user computing devices 111c 1-111 c 3 and/or 112 c 1-112 c 3 may comprise a home assistant deviceand/or personal assistant device, e.g., having display screens,including, by way of non-limiting example, any one or more of a GOOGLEHOME device, an AMAZON ALEXA device, an ECHO SHOW device, or the like.In additional embodiments, user computing devices 111 c 1-111 c 3 and/or112 c 1-112 c 3 may comprise a retail computing device. A retailcomputing device would be configured in the same or similar manner,e.g., as described herein for user computing devices 111 c 1-111 c 3,including having a processor and memory, for implementing, orcommunicating with (e.g., via server(s) 102), a pressure model 108 asdescribed herein. However, a retail computing device may be located,installed, or otherwise positioned within a retail environment to allowusers and/or customers of the retail environment to utilize the digitalimaging systems and methods on site within the retail environment. Forexample, the retail computing device may be installed within a kiosk foraccess by a user. The user may then upload or transfer images (e.g.,from a user mobile device) to the kiosk to implement the digital imagingsystems and methods described herein. Additionally, or alternatively,the kiosk may be configured with a camera to allow the user to take newimages (e.g., in a private manner where warranted) of himself or herselffor upload and transfer. In such embodiments, the user or consumerhimself or herself would be able to use the retail computing device toreceive and/or have rendered a user-specific electronic recommendation,as described herein, on a display screen of the retail computing device.Additionally, or alternatively, the retail computing device may be amobile device (as described herein) as carried by an employee or otherpersonnel of the retail environment for interacting with users orconsumers on site. In such embodiments, a user or consumer may be ableto interact with an employee or otherwise personnel of the retailenvironment, via the retail computing device (e.g., by transferringimages from a mobile device of the user to the retail computing deviceor by capturing new images by a camera of the retail computing device),to receive and/or have rendered a user-specific electronicrecommendation, as described herein, on a display screen of the retailcomputing device. In addition, the one or more user computing devices111 c 1-111 c 3 and/or 112 c 1-112 c 3 may implement or execute anoperating system (OS) or mobile platform such as Apple's iOS and/orGoogle's Android operation system. Any of the one or more user computingdevices 111 c 1-111 c 3 and/or 112 c 1-112 c 3 may comprise one or moreprocessors and/or one or more memories for storing, implementing, orexecuting computing instructions or code, e.g., a mobile application ora home or personal assistant application, as described in variousembodiments herein. As shown in FIG. 1, pressure model 108 may also bestored locally on a memory of a user computing device (e.g., usercomputing device 111 c 1).

User computing devices 111 c 1-111 c 3 and/or 112 c 1-112 c 3 maycomprise a wireless transceiver to receive and transmit wirelesscommunications 121 and/or 122 to and from base stations 111 b and/or 112b. Pixel based images 202 a, 202 b, and/or 202 c may be transmitted viacomputer network 120 to imaging server(s) 102 for training of model(s)and/or imaging analysis as describe herein.

In addition, the one or more user computing devices 111 c 1-111 c 3and/or 112 c 1-112 c 3 may include a digital camera and/or digital videocamera for capturing or taking digital images and/or frames (e.g., whichcan be any one or more of images 202 a, 202 b, and/or 202 c). Eachdigital image may comprise pixel data for training or implementingmodel(s), such as AI or machine learning models, as described herein.For example, a digital camera and/or digital video camera of, e.g., anyof user computing devices 111 c 1-111 c 3 and/or 112 c 1-112 c 3, may beconfigured to take, capture, or otherwise generate digital images (e.g.,pixel based images 202 a, 202 b, and/or 202 c) and, at least in someembodiments, may store such images in a memory of a respective usercomputing devices.

Still further, each of the one or more user computer devices 111 c 1-111c 3 and/or 112 c 1-112 c 3 may include a display screen for displayinggraphics, images, text, product recommendations, data, pixels, features,and/or other such visualizations or information as described herein. Invarious embodiments, graphics, images, text, product recommendations,data, pixels, features, and/or other such visualizations or informationmay be received by imaging server(s) 102 for display on the displayscreen of any one or more of user computer devices 111 c 1-111 c 3and/or 112 c 1-112 c 3. Additionally, or alternatively, a user computerdevice may comprise, implement, have access to, render, or otherwiseexpose, at least in part, an interface or a guided user interface (GUI)for displaying text and/or images on its display screen.

FIGS. 2A-2C illustrate example images 202 a, 202 b, and 202 c that maybe collected or aggregated at imaging server(s) 102 and may be analyzedby, and/or used to train, a pressure model (e.g., an AI model such as amachine learning imaging model as describe herein). Each of these imagesmay comprise pixel data (e.g., RGB data) corresponding representingfeature data and corresponding to each of the personal attributes of therespective users 202 au, 202 bu, and 202 cu, within the respectiveimage. The pixel data may be captured by a digital camera of one of theuser computing devices (e.g., one or more user computer devices 111 c1-111 c 3 and/or 112 c 1-112 c 3).

Generally, as described herein, pixel data (e.g., pixel data 202 ap, 202bp, and/or 202 cp) comprises individual points or squares of data withinan image, where each point or square represents a single pixel (e.g.,pixel 202 ap 1 and pixel 202 ap 2) within an image. Each pixel may be aspecific location within an image. In addition, each pixel may have aspecific color (or lack thereof). Pixel color, may be determined by acolor format and related channel data associated with a given pixel. Forexample, a popular color format includes the red-green-blue (RGB) formathaving red, green, and blue channels. That is, in the RGB format, dataof a pixel is represented by three numerical RGB components (Red, Green,Blue), that may be referred to as a channel data, to manipulate thecolor of pixel's area within the image. In some implementations, thethree RGB components may be represented as three 8-bit numbers for eachpixel. Three 8-bit bytes (one byte for each of RGB) is used to generate24 bit color. Each 8-bit RGB component can have 256 possible values,ranging from 0 to 255 (i.e., in the base 2 binary system, an 8 bit bytecan contain one of 256 numeric values ranging from 0 to 255). Thischannel data (R, G, and B) can be assigned a value from 0 255 and beused to set the pixel's color. For example, three values like (250, 165,0), meaning (Red=250, Green=165, Blue=0), can denote one Orange pixel.As a further example, (Red=255, Green=255, Blue=0) means Red and Green,each fully saturated (255 is as bright as 8 bits can be), with no Blue(zero), with the resulting color being Yellow. As a still furtherexample, the color black has an RGB value of (Red=0, Green=0, Blue=0)and white has an RGB value of (Red=255, Green=255, Blue=255). Gray hasthe property of having equal or similar RGB values. So (Red=220,Green=220, Blue=220) is a light gray (near white), and (Red=40,Green=40, Blue=40) is a dark gray (near black).

In this way, the composite of three RGB values creates the final colorfor a given pixel. With a 24-bit RGB color image using 3 bytes there canbe 256 shades of red, and 256 shades of green, and 256 shades of blue.This provides 256×256×256, i.e., 16.7 million possible combinations orcolors for 24 bit RGB color images. In this way, the pixel's RGB datavalue shows how much of each of Red, and Green, and Blue pixel iscomprised of. The three colors and intensity levels are combined at thatimage pixel, i.e., at that pixel location on a display screen, toilluminate a display screen at that location with that color. In is tobe understood, however, that other bit sizes, having fewer or more bits,e.g., 10-bits, may be used to result in fewer or more overall colors andranges.

As a whole, the various pixels, positioned together in a grid pattern,form a digital image (e.g., pixel data 202 ap, 202 bp, and/or 202 cp). Asingle digital image can comprise thousands or millions of pixels.Images can be captured, generated, stored, and/or transmitted in anumber of formats, such as JPEG, TIFF, PNG and GIF. These formats usepixels to store represent the image.

FIG. 2A illustrates an example image 202 a and its related pixel data(e.g., pixel data 202 ap) that may be used for training and/orimplementing a pressure model (e.g., pressure model 108), in accordancewith various embodiments disclosed herein. Example image 202 aillustrates a user 202 au or individual taking a shaving stroke, with ashaving razor, at a body area location comprising the user's cheek.Image 202 a is comprised of pixel data, including pixel data 202 ap.Pixel data 202 ap includes a plurality of pixels including pixel 202 ap1 and pixel 202 ap 2. Pixel 202 ap 1 is a dark pixel (e.g., a pixel withlow R, G, and B values) positioned in image 202 a where user 202 auapplies pressure to his cheek area with a shaving razor. Pixel 202 ap 2is a pixel positioned in image 202 a comprising a handle portion of theshaving razor. Pixel data 202 ap includes various remaining pixelsincluding remaining portions of the shaving razor and other areas of theuser's cheek where pressure is applied. Pixel data 202 ap furtherincludes pixels representing further features including the shavingstroke, shaving cream affected by the shaving stroke, depression of theuser's skin, the user's hand, and other features as shown in FIG. 2A.

FIG. 2B illustrates a further example image 202 b and its related pixeldata (e.g., pixel data 202 bp) that may be used for training and/orimplementing a pressure model (e.g., pressure model 108), in accordancewith various embodiments disclosed herein. Example image 202 billustrates a user 202 bu or individual taking a shaving stroke, with ashaving razor, at a body area location comprising the user's underarm.Image 202 b is comprised of pixel data, including pixel data 202 bp.Pixel data 202 bp includes a plurality of pixels including pixel 202 bp1 and pixel 202 bp 2. Pixel 202 bp 1 is a dark pixel (e.g., a pixel withlow R, G, and B values) positioned in image 202 b where user 202 auapplies pressure to her underarm area with a shaving razor. Pixel 202 bp2 is a pixel positioned in image 202 b comprising a neck portion of theshaving razor. Pixel data 202 ap includes various remaining pixelsincluding remaining portions of the shaving razor and other areas of theuser's underarm where pressure is applied. Pixel data 202 bp furtherincludes pixels representing further features including the shavingstroke, shaving cream affected by the shaving stroke, depression of theuser's skin, the user's arm, hand position, and other features as shownin FIG. 2B.

FIG. 2C illustrates a further example image 202 cu and its related pixeldata (e.g., 202 cp) that may be used for training and/or implementing apressure model (e.g., pressure model 108), in accordance with variousembodiments disclosed herein. Example image 202 c illustrates a user 202cu or individual taking a shaving stroke, with a shaving razor, at abody area location comprising the user's leg. Image 202 c is comprisedof pixel data, including pixel data 202 cp. Pixel data 202 cp includes aplurality of pixels including pixel 202 cp 1 and pixel 202 cp 2. Pixel202 cp 1 is a dark pixel (e.g., a pixel with low R, G, and B values)positioned in image 202 c where user 202 cu applies pressure to her legarea with a shaving razor. Pixel 202 cp 2 is a pixel positioned in image202 c comprising a handle portion of the shaving razor. Pixel data 202cp includes various remaining pixels including remaining portions of theshaving razor and other areas of the user's leg where pressure isapplied. Pixel data 202 cp further includes pixels representing furtherfeatures including the shaving stroke, shaving cream affected by theshaving stroke, depression of the user's skin, the user's leg, handposition, and other features as shown in FIG. 2C.

FIG. 3 illustrates a diagram of a digital imaging method 300 ofanalyzing pixel data of an image (e.g., any of images 202 a, 202 b,and/or 202 c) of a shaving stroke for determining pressure being appliedto a user's skin, in accordance with various embodiments disclosedherein. Images, as described herein, are generally pixel images ascaptured by a digital camera (e.g., a digital camera of user computingdevice 111 c 1). In some embodiments an image may comprise or refer to aplurality of images such as a plurality of images (e.g., frames) ascollected using a digital video camera. Frames comprise consecutiveimages defining motion, and can comprise a movie, a video, or the like.

At block 302, method 300 comprises aggregating, at one or moreprocessors communicatively coupled to one or more memories, a pluralityof training images of a plurality of individuals, each of the trainingimages comprising pixel data of a respective individual when shavingwith a shaving razor. In some embodiments, the shaving razor may be awet razor. In other embodiments, the shaving razor may be a dry shaver.Additionally, or alternatively, the shaving razor may be an electricrazor, which may be a wet or dry shaving razor.

At block 304, method 300 comprises training, by the one or moreprocessors with the pixel data of the plurality of training images, apressure model (e.g., pressure model 108) operable to determine pressurebeing applied to the respective individual's skin by a shaving razorwhen a shaving stroke is taken. In various embodiments, pressure modelis an artificial intelligence (AI) based model trained with at least oneAI algorithm. Training of pressure model 108 involves image analysis ofthe training images to configure weights of pressure model 108, and itsunderlying algorithm (e.g., machine learning or artificial intelligencealgorithm) used to predict and/or classify future images. For example,in various embodiments herein, generation of pressure model 108 involvestraining pressure model 108 with the plurality of training images of aplurality of individuals, where each of the training images comprisepixel data of a respective individual when shaving with a shaving razor.In some embodiments, one or more processors of a server or a cloud-basedcomputing platform (e.g., imaging server(s) 102) may receive theplurality of training images of the plurality of individuals via acomputer network (e.g., computer network 120). In such embodiments, theserver and/or the cloud-based computing platform may train the pressuremodel with the pixel data of the plurality of training images.

In various embodiments, a machine learning imaging model, as describedherein (e.g. pressure model 108), may be trained using a supervised orunsupervised machine learning program or algorithm. The machine learningprogram or algorithm may employ a neural network, which may be aconvolutional neural network, a deep learning neural network, or acombined learning module or program that learns in two or more featuresor feature datasets (e.g., pixel data) in a particular areas ofinterest. The machine learning programs or algorithms may also includenatural language processing, semantic analysis, automatic reasoning,regression analysis, support vector machine (SVM) analysis, decisiontree analysis, random forest analysis, K-Nearest neighbor analysis,naïve Bayes analysis, clustering, reinforcement learning, and/or othermachine learning algorithms and/or techniques. In some embodiments, theartificial intelligence and/or machine learning based algorithms may beincluded as a library or package executed on imaging server(s) 102. Forexample, libraries may include the TENSORFLOW based library, the PYTORCHlibrary, and/or the SCIKIT-LEARN Python library.

Machine learning may involve identifying and recognizing patterns inexisting data (such as training a model based on pixel data withinimages having pixel data of a respective individual when shaving with ashaving razor) in order to facilitate making predictions oridentification for subsequent data (such as using the model on new pixeldata of a new individual in order to determine a user-specific pressurebeing applied to the specific user's skin by the shaving razor when auser shaving stroke is taken).

Machine learning model(s), such as the pressure model described hereinfor some embodiments, may be created and trained based upon example data(e.g., “training data” and related pixel data) inputs or data (which maybe termed “features” and “labels”) in order to make valid and reliablepredictions for new inputs, such as testing level or production leveldata or inputs. In supervised machine learning, a machine learningprogram operating on a server, computing device, or otherwiseprocessor(s), may be provided with example inputs (e.g., “features”) andtheir associated, or observed, outputs (e.g., “labels”) in order for themachine learning program or algorithm to determine or discover rules,relationships, patterns, or otherwise machine learning “models” that mapsuch inputs (e.g., “features”) to the outputs (e.g., labels), forexample, by determining and/or assigning weights or other metrics to themodel across its various feature categories. Such rules, relationships,or otherwise models may then be provided subsequent inputs in order forthe model, executing on the server, computing device, or otherwiseprocessor(s), to predict, based on the discovered rules, relationships,or model, an expected output.

In unsupervised machine learning, the server, computing device, orotherwise processor(s), may be required to find its own structure inunlabeled example inputs, where, for example multiple trainingiterations are executed by the server, computing device, or otherwiseprocessor(s) to train multiple generations of models until asatisfactory model, e.g., a model that provides sufficient predictionaccuracy when given test level or production level data or inputs, isgenerated. The disclosures herein may use one or both of such supervisedor unsupervised machine learning techniques.

Image analysis may include training a machine learning based model(e.g., the pressure model) on pixel data of images of one or moreindividuals taking shaving strokes with shaving razors. Additionally, oralternatively, image analysis may include using a machine learningimaging model, as previously trained, to determine, based on the pixeldata (e.g., including their RGB values) one or more images of theindividual(s), a user-specific pressure being applied to the user's skinby the shaving razor when a user shaving stroke is taken. The weights ofthe model may be trained via analysis of various RGB values ofindividual pixels of a given image. For example, dark or low RGB values(e.g., a pixel with values R=25, G=28, B=31) may indicate a pressed orloaded area of the user's skin. A red toned RGB value (e.g., a pixelwith values R=215, G=90, B=85) may indicate irritated skin. A lighterRGB values (e.g., a pixel with R=181, G=170, and B=191) may indicate alighter value, such as a normal skin tone color. Together, when a pixelwith a red toned RGB value and/or a pixel with a dark or low RGB valueis positioned within a given image, or is otherwise surrounded by, agroup or set of pixels having skin toned colors, then that may indicatean area on the skin where irritation or pressure occurs, respectively,as identified within the given image. In this way, pixel data (e.g.,detailing one or more features of an individual, such as areas ofpressure applied by a shaving razor or identification of the shavingrazor or its portions) of 10,000s training images may be used to trainor use a machine learning imaging model to determine a user-specificpressure being applied to the user's skin by the shaving razor when auser shaving stroke is taken.

In some embodiments training, by the one or more processors (e.g., ofimaging server(s) 102) with the pixel data of the plurality of trainingimages, the pressure model (e.g., pressure model 108) comprises trainingthe pressure model (e.g., pressure model 108) to detect an amount ofdepression of the user's skin to determine the user-specific pressurebeing applied to the user's skin by the shaving razor when the shavingstroke is taken. In such embodiments the pressure model may be trainedto recognize that pixels with darker values (e.g., darker or lower RGBvalues) indicate a pressure area. For example, for image 202 a, pixel202 ap 1 is a dark pixel positioned in image 202 a where user 202 auapplies pressure to his cheek area with a shaving razor. Pixel 202 ap 2is a pixel positioned in image 202 a comprising a handle portion of theshaving razor. Pixel data 202 ap includes various remaining pixelsincluding remaining portions of the shaving razor and other areas of theuser's cheek where pressure is applied. Pressure model 108 may betrained to recognize (by assigning greater weighs to darker pixels) thatsuch darker pixels (e.g., pixel 202 ap 1) against a pixel or grouppixels having skin tone colors indicates that pressure is being appliedto skin. Similarly, pressure model 108 may be trained to recognize (byassigning greater weighs to nearby angular pixels) that such nearbyangular pixels (e.g., pixel 202 ap 2) represents a shaving razor handle.In this way the pressure model can identify patterns within the pixeldata to determine user-specific pressure being applied to a user's skinby a shaving razor when a user shaving stroke is taken.

Additionally, or alternatively, training, by the one or more processors(e.g., of imaging server(s) 102) with the pixel data of the plurality oftraining images, the pressure model (e.g., pressure model 108) maycomprise training the pressure model (e.g., pressure model 108) todetect an angle of a razor cartridge with respect to a razor handle ofthe shaving razor to determine user-specific pressure being applied tothe user's skin by the shaving razor when the shaving stroke is taken.In such embodiments, multiple pixels distributed across a razor handleof an image (e.g., including pixel 202 ap 2 for image 202 a) may bedetected, e.g., in an angled pattern having a similar RGB color scheme.In this way the pressure model 108 can detect, based on pixels relatedto the razor handle, an angle of a razor cartridge with respect to arazor handle of the shaving razor. The identification of the angle andpositioning of the razor handle (and thereby the razor cartridge) arefeatures or parameters that allow the pressure model (e.g., pressuremodel 108) to determine user-specific pressure being applied to theuser's skin by the shaving razor when the shaving stroke is taken.

In some embodiments, both pressure of the user's skin and angle of therazor handle/razor cartridge may be used to train pressure model 108.For example, in such embodiments, training, by the one or moreprocessors (e.g., imaging server(s) 102) with the pixel data of theplurality of training images, the pressure model (e.g., pressure model108) may comprise training the pressure model (e.g., pressure model 108)to detect an amount of depression of the user's skin (as described aboveherein) in combination with an angle of a razor cartridge with respectto a razor handle of the shaving razor (as described above herein) todetermine the user-specific pressure being applied the a user's skin bythe shaving razor when the shaving stroke is taken.

In various embodiments, a pressure model (e.g., pressure model 108) maybe further trained, by one or more processors (e.g., imaging server(s)102), with the pixel data of the plurality of training images, to outputone or more location identifiers indicating one or more correspondingbody area locations of respective individuals. In such embodiments, thepressure model (e.g., pressure model 108), executing on the one or moreprocessors (e.g., imaging server(s) 102) and analyzing the at least oneimage of the user, can determine a location identifier indicating a bodyarea location of the user's body or body area. For example, body arealocations may comprise a user's cheek, a user's neck, a user's head, auser's groin, a user's underarm, a user's chest, a user's back, a user'sleg, a user's arm, or a user's bikini area. For example, each of imagesimage 202 a, 202 b, and 202 c illustrate example body area locationsincluding a user's check, a user's underarm or armpit, and a user's leg,respectively.

Additionally, or alternatively, training, by the one or more processors(e.g., of imaging server(s) 102) with the pixel data of the plurality oftraining images, the pressure model (e.g., pressure model 108) maycomprise training the pressure model (e.g., pressure model 108) todetect an amount of depression of the user's skin in combination with adirection in which a razor handle of the shaving razor is orientedrelative to a body area of the user to determine the user-specificpressure being applied the a user's skin by the shaving razor when theshaving stroke is taken. In such embodiments, multiple pixelsdistributed across a razor handle of an image (e.g., including pixel 202ap 2 for image 202 a) may be detected, e.g., in an angled pattern havinga similar RGB color scheme. The detected pixels may be compared with abody area (e.g., a chin or neck) to determine the orientation of theshaving razor to the body area. For example, the pixel data couldindicate that a razor handle is orientated with a non-cartridge/razorend in an upwards orientation (upstroke) with respect to the chin orneck area. Alternatively, the pixel data could indicate that a razorhandle is orientated with a non-cartridge/razor end in a downwardsorientation (down stroke) with respect to the chin or neck area. Othersuch orientations could include a sideways orientation and/or relateddegrees thereof. In this way the pressure model 108 can detect, based onpixels related to the razor handle oriented relative to a body area ofthe user, a direction in which a razor handle is moving or is otherwiseoriented. The identification of the orientation and direction of therazor handle (and thereby the razor cartridge) relative to the body areaare features or parameters that allow the pressure model (e.g., pressuremodel 108) to determine user-specific pressure being applied to theuser's skin by the shaving razor when the shaving stroke is taken.

Additionally, or alternatively, training, by the one or more processors(e.g., of imaging server(s) 102) with the pixel data of the plurality oftraining images, the pressure model (e.g., pressure model 108) maycomprise training the pressure model (e.g., pressure model 108) todetect an amount of depression of the user's skin in combination with ahand grip pattern of the user on a razor handle of the shaving razor todetermine the user-specific pressure being applied the a user's skin bythe shaving razor when the shaving stroke is taken. In such embodiments,multiple pixels distributed across a razor handle of an image (e.g.,including pixel 202 ap 2 for image 202 a) may be detected, e.g., in anangled pattern having a similar RGB color scheme. The detected pixelsmay be compared with an identified hand grip pattern (e.g.,identification of fingers on the razor handle) to determine pressurebeing applied to the user skin. For example, a strong grip pattern(e.g., more fingers detected on the razor handle within the pixel data)could indicate more pressure is applied to the user's skin and viceversa. In this way the pressure model 108 can detect, based on pixelscomprising a hand grip pattern of the user on the razor handle of theshaving razor, pressure applied. The hand grip pattern and razor handleare features or parameters that allow the pressure model (e.g., pressuremodel 108) to determine user-specific pressure being applied to theuser's skin by the shaving razor when the shaving stroke is taken.

With reference to FIG. 3, at block 306 method 300 comprises receiving,at the one or more processors (e.g., imaging server(s) 102 and/or a usercomputing device, such as user computing device 111 c 1), at least oneimage of a user when shaving with the shaving razor. The at least oneimage may have been captured by a digital camera. In addition, the atleast one image may comprise pixel data of at least a portion of theshaving razor applied to the user's skin.

At block 308, method 300 comprises analyzing, by the pressure model(e.g., pressure model 108) executing on the one or more processors(e.g., imaging server(s) 102 and/or a user computing device, such asuser computing device 111 c 1), the at least one image captured by thedigital camera to determine a user-specific pressure being applied tothe user's skin by the shaving razor when a user shaving stroke istaken.

At block 310, method 300 comprises generating, by the one or moreprocessors (e.g., imaging server(s) 102 and/or a user computing device,such as user computing device 111 c 1) based on the user-specificpressure, at least one user-specific electronic recommendation designedto address at least one feature identifiable within the pixel datacomprising the at least the portion of the shaving razor applied to theuser's skin.

At block 312, method 300 comprises rendering, on a display screen of auser computing device, the at least one user-specific recommendation. Auser computing device may comprise at least one of a mobile device, atablet, a handheld device, or a desktop device, for example, asdescribed herein for FIG. 1. In some embodiments, the user computingdevice (e.g., user computing device 111 c 1) may receive the at leastone image comprising the pixel data of the at least a portion of theshaving razor applied to the user's skin. In such embodiments, the usercomputing device may execute the pressure model (e.g., pressure model108) locally and generate, based on output of the pressure model (e.g.,pressure model 108), the user-specific recommendation. The usercomputing device 111 c 1 may then render the user-specificrecommendation on its display screen.

Additionally, or alternatively, in other embodiments, the imagingserver(s) 102 may analyze the user image remote from the user computingdevice to determine the user-specific pressure and/or user-specificelectronic recommendation designed to address at least one featureidentifiable within the pixel data comprising the at least the portionof the shaving razor applied to the user's skin. For example, in suchembodiments imaging server or a cloud-based computing platform (e.g.,imaging server(s) 102) receives, across computer network 120, the atleast one image comprising the pixel data of at the least a portion ofthe shaving razor applied to the user's skin. The server or acloud-based computing platform may then execute pressure model (e.g.,pressure model 108) and generate, based on output of the pressure model(e.g., pressure model 108), the user-specific recommendation. The serveror a cloud-based computing platform may then transmit, via the computernetwork (e.g., computer network 120), the user-specific recommendationto the user computing device for rendering on the display screen of theuser computing device

In some embodiments, the user may submit a new image to the pressuremodel for analysis as described herein. In such embodiments, one or moreprocessors (e.g., imaging server(s) 102 and/or a user computing device,such as user computing device 111 c 1) may receive a new image of theuser when shaving with the shaving razor. The new image may beencaptured by a digital camera of user computing device 111 c 1. The newimage may comprise pixel data of at least a portion of the shaving razorapplied to the user's skin. The pressure model (e.g., pressure model108) may then analyze, on the one or more processors (e.g., imagingserver(s) 102 and/or a user computing device, such as user computingdevice 111 c 1), the new image captured by the digital camera todetermine a new user-specific pressure being applied to the user's skinby the shaving razor when a new user shaving stroke is taken. A newuser-specific electronic recommendation or comment may be generated,based on the new user-specific pressure, regarding at least one featureidentifiable within the pixel data of the new image. The newuser-specific recommendation or comment (e.g., message) may then berendered on a display screen of a user computing device of the user.

In some embodiments, a user-specific electronic recommendation may bedisplayed on the display screen of a user computing device (e.g., usercomputing device 111 c 1) with a graphical representation of the user'sskin as annotated with one or more graphics or textual renderingscorresponding to the user-specific pressure. In still furtherembodiments, the at least one user-specific electronic recommendationmay be rendered in real-time or near-real time during or after the usershaving stroke is taken.

In additional embodiments, a user-specific electronic recommendation maycomprise a product recommendation for a manufactured product. In suchembodiments, the user-specific electronic recommendation may bedisplayed on the display screen of a user computing device (e.g., usercomputing device 111 c 1) with instructions (e.g., a message) fortreating, with the manufactured product, the at least one featureidentifiable in the pixel data comprising the at least the portion ofthe shaving razor applied to the user's skin. In still furtherembodiments, either the user computing device 111 c 1 and/or imagingserver(s) may initiate, based on the product recommendation, themanufactured product for shipment to the user.

With regard to manufactured product recommendations, in someembodiments, one or more processors (e.g., imaging server(s) 102 and/ora user computing device, such as user computing device 111 c 1) maygenerate a modified image based on the at least one image of the user,e.g., as originally received. In such embodiments, the modified imagemay depict a rendering of how the user's skin is predicted to appearafter treating the at least one feature with the manufactured product.For example, the modified image may be modified by updating, smoothing,or changing colors of the pixels of the image to represent a possible orpredicted change after treatment of the at least one feature within thepixel data with the manufactured product. The modified image may then berendered on the display screen of the user computing device (e.g., usercomputing device 111 c 1).

Additionally, or alternatively, a recommendation may be also made forthe shaving razor shown in the at least one image of the user, e.g., asoriginally received. In such embodiments, a user-specific electronicrecommendation may displayed on the display screen of the user computingdevice (e.g., user computing device 111 c 1) with instructions fortreating, with the shaving razor (e.g., as shown in the original image),the at least one feature identifiable in the pixel data comprising theat least the portion of the shaving razor applied to the user's skin.

FIG. 4 illustrates an example user interface 402 as rendered on adisplay screen 400 of a user computing device 111 c 1 in accordance withvarious embodiments disclosed herein. For example, as shown in theexample of FIG. 4, user interface 402 may be implemented or rendered viaan application (app) executing on user computing device 111 c 1.

For example, as shown in the example of FIG. 4, user interface 402 maybe implemented or rendered via a native app executing on user computingdevice 111 c 1. In the example of FIG. 4, user computing device 111 c 1is a user computer device as described for FIG. 1, e.g., where 111 c 1is illustrated as an APPLE iPhone that implements the APPLE iOSoperating system and has display screen 400. User computing device 111 c1 may execute one or more native applications (apps) on its operatingsystem. Such native apps may be implemented or coded (e.g., as computinginstructions) in a computing language (e.g., SWIFT) executable by theuser computing device operating system (e.g., APPLE iOS) by theprocessor of user computing device 111 c 1.

Additionally, or alternatively, user interface 402 may be implemented orrendered via a web interface, such as via a web browser application,e.g., Safari and/or Google Chrome app(s), or other such web browser orthe like.

As shown in the example of FIG. 4, user interface 402 comprises agraphical representation (e.g., image 202 a) of the user's skin. Image202 a may be the at least one image of the user (or graphicalrepresentation thereof) when shaving with the shaving razor and asanalyzed by the pressure model (e.g., pressure model 108) as describedherein. In the example of FIG. 4, graphical representation (e.g., image202 a) of the user's skin is annotated with one or more graphics (e.g.,area of pixel data 202 ap) or textual rendering (e.g., text 202 at)corresponding to the user-specific pressure. For example, the area ofpixel data 202 ap may be annotated or overlaid on top of the image ofthe user (e.g., image 202 a) to highlight the area or feature(s)identified within the pixel data (e.g., feature data and/or raw pixeldata) by the pressure model (e.g., pressure model 108). In the exampleof FIG. 4, the area of pixel data 202 ap and the feature(s) identifiedwithin include the user-specific pressure the user is applying with hisshaving stroke, the shaving razor, the depression of the user's skin,irritation of the skin, skin type, skin tone, direction of the shavingstroke with respect to hair grain, and other features shown in area ofpixel data 202 ap. In various embodiments, the pixels identified as thespecific features indicating pressure (e.g., pixel 202 ap 1 as a darkpixel indicating where pressure is applied) or causing pressure (e.g.,e.g., pixel 202 ap 2 indicating the handle or neck of the razor causingpressure) may be highlighted or otherwise annotated when rendered.

Textual rendering (e.g., text 202 at) shows a user-specific pressurevalue (e.g., 120%) which illustrates that the user is applying 120% of aneeded or recommended pressure for the given shaving stroke. The 120%value indicates that the user is applying too much pressure that willlikely lead to skin irritation. It is to be understood that othertextual rendering types or values are contemplated herein, where textualrendering types or values may be rendered, for example, as pressurevalues in, e.g., Pascal (Pa) values, newton per square meter (N/m2)values, pounds per square inch (psi), or the like. Additionally, oralternatively, color values may use and/or overlaid on a graphicalrepresentation shown on user interface 402 (e.g., image 202 a) toindicate too much pressure, too little pressure, or pressure withinacceptable ranges (e.g., 95% to 103% pressure).

User interface 402 may also include or render a user-specific electronicrecommendation 412. In the embodiment of FIG. 4, user-specificelectronic recommendation 412 comprises a message 412 m to the userdesigned to address at least one feature identifiable within the pixeldata comprising the portion of the shaving razor applied to the user'sskin. As shown in the example of FIG. 4, message 412 m recommends to theuser to apply less pressure (e.g., 20% less pressure) and to apply theshaving stroke against the skin of the user.

Message 412 m further recommends use of a shaving gel to reduce skinirritation. The shaving gel recommendation can be made based on the highpressure value (e.g., 120%) that the user is applying where the shavinggel product is designed to address the issue of skin irritation detectedin the pixel data of image 202 a or otherwise assumed based on the highpressure value. The product recommendation can be correlated to theidentified feature within the pixel data, and the user computing device111 c 1 and/or server(s) 102 can be instructed to output the productrecommendation when the feature (e.g., excessive pressure or skinirritation) is identified.

User interface 402 also include or render a section for a productrecommendation 422 for a manufactured product 424 r (e.g., shaving gelas described above). The product recommendation 422 generallycorresponds to the user-specific electronic recommendation 412, asdescribed above. For example, in the example of FIG. 4, theuser-specific electronic recommendation 412 is displayed on displayscreen 400 of user computing device 111 c 1 with instructions (e.g.,message 412 m) for treating, with the manufactured product (manufacturedproduct 424 r (e.g., shaving gel)) at least one feature (e.g., 120%pressure applied at pixel 202 ap 1) identifiable in the pixel data(e.g., pixel data 202 ap) comprising the at least the portion of theshaving razor (e.g., pixel 202 ap 2) applied to the user's skin.

As shown in FIG. 4, user interface 402 recommends a product (e.g.,manufactured product 424 r (e.g., shaving gel)) based on theuser-specific electronic recommendation 412. In the example of FIG. 4,the output or analysis of image(s) (e.g. image 202 a) of pressure model(e.g., pressure model 108), e.g., user-specific electronicrecommendation 412 and/or its related values (e.g., 120% pressureapplied) or related pixel data (e.g., 202 ap 1 and/or 202 ap 2), may beused to generate or identify recommendations for correspondingproduct(s). Such recommendations may include products such as shavinggel, new shaving razor(s), shaving blade(s), a moisturizing treatment,or the like to address the user-specific issue as detected within thepixel data by the pressure model (e.g., pressure model 108).

In the example of FIG. 4, user interface 402 renders or provides arecommended product (e.g., manufactured product 424 r) as determined bypressure model (e.g., pressure model 108) and its related image analysisof image 202 a and its pixel data and various features. In the exampleof FIG. 4, this is indicated and annotated (424 p) on user interface402.

User interface 402 may further include a selectable UI button 424 s toallow the user (e.g., the user of image 202 a) to select for purchase orshipment the corresponding product (e.g., manufactured product 424 r).In some embodiments, selection of selectable UI button 424 s a may causethe recommended product(s) to be shipped to the user (e.g., individual501) and/or may notify a third party that the individual is interestedin the product(s). For example, either user computing device 111 c 1and/or imaging server(s) 102 may initiate, based on user-specificelectronic recommendation 412, the manufactured product 424 r (e.g.,shaving gel) for shipment to the user. In such embodiments, the productmay be packaged and shipped to the user.

In various embodiments, graphical representation (e.g., image 202 a),with graphical annotations (e.g., area of pixel data 202 ap), textualannotations (e.g., text 202 at), user-specific electronic recommendation412 may be transmitted, via the computer network (e.g., from an imagingserver 102 and/or one or more processors) to user computing device 111 c1, for rendering on display screen 400. In other embodiments, notransmission to the imaging server of the user's specific image occurs,where the user-specific recommendation (and/or product specificrecommendation) may instead be generated locally, by the pressure model(e.g., pressure model 108) executing and/or implemented on the user'smobile device (e.g., user computing device 111 c 1) and rendered, by aprocessor of the mobile device, on display screen 400 of the mobiledevice (e.g., user computing device 111 c 1).

In some embodiments, any one or more of graphical representations (e.g.,image 202 a), with graphical annotations (e.g., area of pixel data 202ap), textual annotations (e.g., text 202 at), user-specific electronicrecommendation 412, and/or product recommendation 422 may be rendered(e.g., rendered locally on display screen 400) in real-time or near-realtime during or after the user shaving stroke is taken. In embodimentswhere the image is analyzed by imaging server(s) 102, the image may betransmitted and analyzed in real-time or near real-time by imagingserver(s) 102.

In some embodiments, the user may provide a new image that may betransmitted to imaging server(s) 102 for updating, retraining, orreanalyzing by pressure model 108. In other embodiments, a new imagethat may be locally received on computing device 111 c 1 and analyzed,by pressure model 108, on the computing device 111 c 1.

In addition, as shown in the example of FIG. 4, the user may selectselectable button 412 i to for reanalyzing (e.g., either locally atcomputing device 111 c 1 or remotely at imaging server(s) 102) a newimage. Selectable button 412 i may cause user interface 402 to promptthe user to attach for analyzing a new image. Imaging server(s) 102and/or a user computing device such as user computing device 111 c 1 mayreceive a new image of the user when shaving with the shaving razor. Thenew image may be captured by the digital camera. The new image (e.g.,just like image 202 a) may comprise pixel data of at least a portion ofthe shaving razor applied to the user's skin. The pressure model (e.g.,pressure model 108), executing on the memory of the computing device(e.g., imaging server(s) 102), may analyze the new image captured by thedigital camera to determine a new user-specific pressure being appliedto the user's skin by the shaving razor when a new user shaving strokeis taken. The computing device (e.g., imaging server(s) 102) maygenerate, based on the new user-specific pressure, a new user-specificelectronic recommendation or comment regarding at least one featureidentifiable within the pixel data of the new image. For example the newuser-specific electronic recommendation may include a new graphicalrepresentation including graphics and/or text (e.g., showing a newuser-specific pressure value, e.g., 80%). The new user-specificelectronic recommendation may include additional recommendations, e.g.,that the user has overcorrected by applying too little pressure (e.g.,at 80% of recommended pressure) as detected with the pixel data of thenew image. A comment may include that the user has corrected the atleast one feature identifiable within the pixel data (e.g., theuser-specific pressure is now correct between 95% and 105%).

In some embodiments, a delta pressure value may be generated, by the oneor more processors (e.g., a processor of imaging server(s) 102 and/oruser computing device such as user computing device 111 c 1) based on acomparison between the new user-specific pressure and the user-specificpressure. In such embodiments, the new user-specific recommendation orcomment may be further based on the delta pressure value. The deltapressure value, a representation of the delta pressure value (e.g., agraph or other graphical depiction), or a comment (e.g., text) based onthe delta pressure value, may be rendered on the display screen of theuser computing device (e.g., user computing device 111 c 1) toillustrate or describe the difference (delta) between the newuser-specific pressure and the user-specific pressure as previouslydetermined.

In various embodiments, the new user-specific recommendation or commentmay be transmitted via the computer network, from server(s) 102, to theuser computing device of the user for rendering on the display screen ofthe user computing device.

In other embodiments, no transmission to the imaging server of theuser's new image occurs, where the new user-specific recommendation(and/or product specific recommendation) may instead be generatedlocally, by the pressure model (e.g., pressure model 108) executingand/or implemented on the user's mobile device (e.g., user computingdevice 111 c 1) and rendered, by a processor of the mobile device, on adisplay screen of the mobile device (e.g., user computing device 111 c1).

ASPECTS OF THE DISCLOSURE

The following aspects are provided as examples in accordance with thedisclosure herein and are not intended to limit the scope of thedisclosure.

1. A digital imaging method of analyzing pixel data of an image of ashaving stroke for determining pressure being applied to a user's skin,the digital imaging method comprising the steps of: (a) aggregating, atone or more processors communicatively coupled to one or more memories,a plurality of training images of a plurality of individuals, each ofthe training images comprising pixel data of a respective individualwhen shaving with a shaving razor; (b) training, by the one or moreprocessors with the pixel data of the plurality of training images, apressure model operable to determine pressure being applied to therespective individual's skin by a shaving razor when a shaving stroke istaken; (c) receiving, at the one or more processors, at least one imageof a user when shaving with the shaving razor, the at least one imagecaptured by a digital camera, and the at least one image comprisingpixel data of at least a portion of the shaving razor applied to theuser's skin; (d) analyzing, by the pressure model executing on the oneor more processors, the at least one image captured by the digitalcamera to determine a user-specific pressure being applied to the user'sskin by the shaving razor when a user shaving stroke is taken; (e)generating, by the one or more processors based on the user-specificpressure, at least one user-specific electronic recommendation designedto address at least one feature identifiable within the pixel datacomprising the at least the portion of the shaving razor applied to theuser's skin; and (f) rendering, on a display screen of a user computingdevice, the at least one user-specific recommendation.

2. The digital imaging method of aspect 1, wherein the at least oneuser-specific electronic recommendation is displayed on the displayscreen of the user computing device with a graphical representation ofthe user's skin as annotated with one or more graphics or textualrenderings corresponding to the user-specific pressure.

3. The digital imaging method of any one of aspects 1-2, wherein the atleast one user-specific electronic recommendation is rendered inreal-time or near-real time, during, or after the user shaving stroke istaken.

4. The digital imaging method of any one of aspects 1-3, wherein the atleast one user-specific electronic recommendation comprises a productrecommendation for a manufactured product.

5. The digital imaging method of aspect 4, wherein the at least oneuser-specific electronic recommendation is displayed on the displayscreen of the user computing device with instructions for treating, withthe manufactured product, the at least one feature identifiable in thepixel data comprising the at least the portion of the shaving razorapplied to the user's skin.

6. The digital imaging method of aspect 4, further comprising the stepsof: initiating, based on the product recommendation, the manufacturedproduct for shipment to the user.

7. The digital imaging method of aspect 4, further comprising the stepsof: generating, by the one or more processors, a modified image based onthe at least one image, the modified image depicting how the user's skinis predicted to appear after treating the at least one feature with themanufactured product; and rendering, on the display screen of the usercomputing device, the modified image.

8. The digital imaging method of any one of aspects 1-7, wherein the atleast one user-specific electronic recommendation is displayed on thedisplay screen of the user computing device with instructions fortreating, with the shaving razor, the at least one feature identifiablein the pixel data comprising the at least the portion of the shavingrazor applied to the user's skin.

9. The digital imaging method of any one of aspects 1-8, wherein thepressure model is an artificial intelligence (AI) based model trainedwith at least one AI algorithm.

10. The digital imaging method of any one of aspects 1-9, wherein thepressure model is further trained, by the one or more processors withthe pixel data of the plurality of training images, to output one ormore location identifiers indicating one or more corresponding body arealocations of respective individuals, and wherein the pressure model,executing on the one or more processors and analyzing the at least oneimage of the user, determines a location identifier indicating a bodyarea location of the user's body or body area.

11. The digital method of aspect 10, wherein the body area locationcomprises the user's cheek, the user's neck, the user's head, the user'sgroin, the user's underarm, the user's chest, the user's back, theuser's leg, the user's arm, or the user's bikini area.

12. The digital method of any one of aspects 1-11, wherein training, bythe one or more processors with the pixel data of the plurality oftraining images, the pressure model comprises training the pressuremodel to detect an amount of depression of the user's skin to determinethe user-specific pressure being applied to the user's skin by theshaving razor when the shaving stroke is taken.

13. The digital method of any one of aspects 1-12, wherein training, bythe one or more processors with the pixel data of the plurality oftraining images, the pressure model comprises training the pressuremodel to detect an angle of a razor cartridge with respect to a razorhandle of the shaving razor to determine the user-specific pressurebeing applied to the user's skin by the shaving razor when the shavingstroke is taken.

14. The digital method of any one of aspects 1-13, wherein training, bythe one or more processors with the pixel data of the plurality oftraining images, the pressure model comprises training the pressuremodel to detect an amount of depression of the user's skin incombination with an angle of a razor cartridge with respect to a razorhandle of the shaving razor to determine the user-specific pressurebeing applied the a user's skin by the shaving razor when the shavingstroke is taken.

15. The digital method of any one of aspects 1-14, wherein training, bythe one or more processors with the pixel data of the plurality oftraining images, the pressure model comprises training the pressuremodel to detect an amount of depression of the user's skin incombination with a direction in which a razor handle of the shavingrazor is oriented relative to a body area of the user to determine theuser-specific pressure being applied the a user's skin by the shavingrazor when the shaving stroke is taken.

16. The digital method of any one of aspects 1-15, wherein training, bythe one or more processors with the pixel data of the plurality oftraining images, the pressure model comprises training the pressuremodel to detect an amount of depression of the user's skin incombination with a hand grip pattern of the user on a razor handle ofthe shaving razor to determine the user-specific pressure being appliedthe a user's skin by the shaving razor when the shaving stroke is taken.

17. The digital method of any one of aspects 1-16, further comprising:receiving, at the one or more processors, a new image of the user whenshaving with the shaving razor, the new image captured by the digitalcamera, and the new image comprising pixel data of at least a portion ofthe shaving razor applied to the user's skin; analyzing, by the pressuremodel executing on the one or more processors, the new image captured bythe digital camera to determine a new user-specific pressure beingapplied to the user's skin by the shaving razor when a new user shavingstroke is taken; generating, based on the new user-specific pressure, anew user-specific electronic recommendation or comment regarding atleast one feature identifiable within the pixel data of the new image;and rendering, on a display screen of a user computing device of theuser, the new user-specific recommendation or comment.

18. The digital imaging method of claim 17, wherein a delta pressurevalue is generated based on a comparison between the new user-specificpressure and the user-specific pressure, wherein the new user-specificrecommendation or comment is further based on the delta pressure value,and wherein the delta pressure value, a representation of the deltapressure value, or a comment based on the delta pressure value, isrendered on the display screen of the user computing device.

19. The digital method of any one of aspects 1-18, wherein the one ormore processors comprises at least one of a server or a cloud-basedcomputing platform, and the server or the cloud-based computing platformreceives the plurality of training images of the plurality ofindividuals via a computer network, and wherein the server or thecloud-based computing platform trains the pressure model with the pixeldata of the plurality of training images.

20. The digital method of aspect 19, wherein the server or a cloud-basedcomputing platform receives the at least one image comprising the pixeldata of at the least a portion of the shaving razor applied to theuser's skin, and wherein the server or a cloud-based computing platformexecutes the pressure model and generates, based on output of thepressure model, the user-specific recommendation and transmits, via thecomputer network, the user-specific recommendation to the user computingdevice for rendering on the display screen of the user computing device.

21. The digital method of any one of aspects 1-20, wherein the usercomputing device comprises at least one of a mobile device, a tablet, ahandheld device, a desktop device, a home assistant device, or apersonal assistant device.

22. The digital method of any one of aspects 1-21, wherein the usercomputing device receives the at least one image comprising the pixeldata of the at least a portion of the shaving razor applied to theuser's skin, and wherein the user computing device executes the pressuremodel and generates, based on output of the pressure model, theuser-specific recommendation, and renders the user-specificrecommendation on the display screen of the user computing device.

23. The digital method of any one of aspects 1-22, wherein the at leastone image comprises a plurality of images.

24. The digital method of any one of aspects 23, wherein the pluralityof images are collected using a digital video camera.

25. The digital method of any one of aspects 1-24, wherein the shavingrazor is a wet razor, a dry shaver, or an electric razor.

26. A digital imaging system configured to analyze pixel data of animage of a shaving stroke for determining pressure being applied to auser's skin, the digital imaging system comprising: an imaging servercomprising a server processor and a server memory; an imagingapplication (app) configured to execute on a user computing devicecomprising a device processor and a device memory, the imaging appcommunicatively coupled to the imaging server; and a pressure modeltrained with pixel data of a plurality of training images of individualsand operable to determine pressure being applied to a respectiveindividual's skin by a shaving razor when a shaving stroke is taken,wherein the pressure model is configured to execute on the serverprocessor or the device processor to cause the server processor or thedevice processor to: receive at least one image of a user when shavingwith the shaving razor, the at least one image captured by a digitalcamera, and the at least one image comprising pixel data of at least aportion of the shaving razor applied to the user's skin, analyze, by thepressure model, the at least one image captured by the digital camera todetermine a user-specific pressure being applied to the user's skin bythe shaving razor when a user shaving stroke is taken, generate, basedon the user-specific pressure, at least one user-specific electronicrecommendation designed to address at least one feature identifiablewithin the pixel data comprising the at least the portion of the shavingrazor applied to the user's skin, and render, on a display screen of theuser computing device of the user, the at least one user-specificrecommendation.

27. A tangible, non-transitory computer-readable medium storinginstructions for analyzing pixel data of an image of a shaving strokefor determining pressure being applied to a user's skin, that whenexecuted by one or more processors cause the one or more processors to:(a) aggregate, at one or more processors communicatively coupled to oneor more memories, a plurality of training images of a plurality ofindividuals, each of the training images comprising pixel data of arespective individual when shaving with a shaving razor; (b) train, bythe one or more processors with the pixel data of the plurality oftraining images, a pressure model operable to determine pressure beingapplied to the respective individual's skin by a shaving razor when ashaving stroke is taken; (c) receive, at the one or more processors, atleast one image of a user when shaving with the shaving razor, the atleast one image captured by a digital camera, and the at least one imagecomprising pixel data of at least a portion of the shaving razor appliedto the user's skin; (d) analyze, by the pressure model executing on theone or more processors, the at least one image captured by the digitalcamera to determine a user-specific pressure being applied to the user'sskin by the shaving razor when a user shaving stroke is taken; (e)generate, by the one or more processors based on the user-specificpressure, at least one user-specific electronic recommendation designedto address at least one feature identifiable within the pixel datacomprising the at least the portion of the shaving razor applied to theuser's skin; and (f) render, on a display screen of a user computingdevice, the at least one user-specific recommendation.

Additional Considerations

Although the disclosure herein sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the description is defined by the words of the claims set forthat the end of this patent and equivalents. The detailed description isto be construed as exemplary only and does not describe every possibleembodiment since describing every possible embodiment would beimpractical. Numerous alternative embodiments may be implemented, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

The following additional considerations apply to the foregoingdiscussion. Throughout this specification, plural instances mayimplement components, operations, or structures described as a singleinstance. Although individual operations of one or more methods areillustrated and described as separate operations, one or more of theindividual operations may be performed concurrently, and nothingrequires that the operations be performed in the order illustrated.Structures and functionality presented as separate components in exampleconfigurations may be implemented as a combined structure or component.Similarly, structures and functionality presented as a single componentmay be implemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium or in a transmission signal) or hardware.In hardware, the routines, etc., are tangible units capable ofperforming certain operations and may be configured or arranged in acertain manner. In example embodiments, one or more computer systems(e.g., a standalone, client or server computer system) or one or morehardware modules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location, while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In otherembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. A person of ordinaryskill in the art may implement numerous alternate embodiments, usingeither current technology or technology developed after the filing dateof this application.

Those of ordinary skill in the art will recognize that a wide variety ofmodifications, alterations, and combinations can be made with respect tothe above described embodiments without departing from the scope of theinvention, and that such modifications, alterations, and combinationsare to be viewed as being within the ambit of the inventive concept.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s). Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

The dimensions and values disclosed herein are not to be understood asbeing strictly limited to the exact numerical values recited. Instead,unless otherwise specified, each such dimension is intended to mean boththe recited value and a functionally equivalent range surrounding thatvalue. For example, a dimension disclosed as “40 mm” is intended to mean“about 40 mm.”

Every document cited herein, including any cross referenced or relatedpatent or application and any patent application or patent to which thisapplication claims priority or benefit thereof, is hereby incorporatedherein by reference in its entirety unless expressly excluded orotherwise limited. The citation of any document is not an admission thatit is prior art with respect to any invention disclosed or claimedherein or that it alone, or in any combination with any other referenceor references, teaches, suggests or discloses any such invention.Further, to the extent that any meaning or definition of a term in thisdocument conflicts with any meaning or definition of the same term in adocument incorporated by reference, the meaning or definition assignedto that term in this document shall govern.

While particular embodiments of the present invention have beenillustrated and described, it would be obvious to those skilled in theart that various other changes and modifications can be made withoutdeparting from the spirit and scope of the invention. It is thereforeintended to cover in the appended claims all such changes andmodifications that are within the scope of this invention.

What is claimed is:
 1. A digital imaging method of analyzing pixel dataof an image of a shaving stroke for determining pressure being appliedto a user's skin, the digital imaging method comprising the steps of: a.aggregating, at one or more processors communicatively coupled to one ormore memories, a plurality of training images of a plurality ofindividuals, each of the training images comprising pixel data of arespective individual when shaving with a shaving razor; b. training, bythe one or more processors with the pixel data of the plurality oftraining images, a pressure model operable to determine pressure beingapplied to the respective individual's skin by a shaving razor when ashaving stroke is taken; c. receiving, at the one or more processors, atleast one image of a user when shaving with the shaving razor, the atleast one image captured by a digital camera, and the at least one imagecomprising pixel data of at least a portion of the shaving razor appliedto the user's skin; d. analyzing, by the pressure model executing on theone or more processors, the at least one image captured by the digitalcamera to determine a user-specific pressure being applied to the user'sskin by the shaving razor when a user shaving stroke is taken; e.generating, by the one or more processors based on the user-specificpressure, at least one user-specific electronic recommendation designedto address at least one feature identifiable within the pixel datacomprising the at least the portion of the shaving razor applied to theuser's skin; f. rendering, on a display screen of a user computingdevice, the at least one user-specific recommendations; g. receiving, atthe one or more processors, a new image of the user when shaving withthe shaving razor, the new image captured by the digital camera, and thenew image comprising pixel data of at least a portion of the shavingrazor applied to the user's skin; h. analyzing, by the pressure modelexecuting on the one or more processors, the new image captured by thedigital camera to determine a new user-specific pressure being appliedto the user's skin by the shaving razor when a new user shaving strokeis taken; i. generating, based on the new user-specific pressure, a newuser-specific electronic recommendation or comment regarding at leastone feature identifiable within the pixel data of the new image; and j.rendering, on a display screen of a user computing device of the user,the new user-specific recommendation or comment, wherein a deltapressure value is generated based on a comparison between the newuser-specific pressure and the user-specific pressure, wherein the newuser-specific recommendation or comment is further based on the deltapressure value, and wherein the delta pressure value, a representationof the delta pressure value, or a comment based on the delta pressurevalue, is rendered on the display screen of the user computing device.2. The digital imaging method of claim 1, wherein the at least oneuser-specific electronic recommendation is displayed on the displayscreen of the user computing device with a graphical representation ofthe user's skin as annotated with one or more graphics or textualrenderings corresponding to the user-specific pressure.
 3. The digitalimaging method of claim 1, wherein the at least one user-specificelectronic recommendation is rendered in real-time or near-real time,during, or after the user shaving stroke is taken.
 4. The digitalimaging method of claim 1, wherein the at least one user-specificelectronic recommendation comprises a product recommendation for amanufactured product.
 5. The digital imaging method of claim 4, whereinthe at least one user-specific electronic recommendation is displayed onthe display screen of the user computing device with instructions fortreating, with the manufactured product, the at least one featureidentifiable in the pixel data comprising the at least the portion ofthe shaving razor applied to the user's skin.
 6. The digital imagingmethod of claim 4, further comprising the steps of: initiating, based onthe product recommendation, the manufactured product for shipment to theuser.
 7. The digital imaging method of claim 4, further comprising thesteps of: generating, by the one or more processors, a modified imagebased on the at least one image, the modified image depicting how theuser's skin is predicted to appear after treating the at least onefeature with the manufactured product; and rendering, on the displayscreen of the user computing device, the modified image.
 8. The digitalimaging method of claim 1, wherein the at least one user-specificelectronic recommendation is displayed on the display screen of the usercomputing device with instructions for treating, with the shaving razor,the at least one feature identifiable in the pixel data comprising theat least the portion of the shaving razor applied to the user's skin. 9.The digital imaging method of claim 1, wherein the pressure model is anartificial intelligence (AI) based model trained with at least one AIalgorithm.
 10. The digital imaging method of claim 1, wherein thepressure model is further trained, by the one or more processors withthe pixel data of the plurality of training images, to output one ormore location identifiers indicating one or more corresponding body arealocations of respective individuals, and wherein the pressure model,executing on the one or more processors and analyzing the at least oneimage of the user, determines a location identifier indicating a bodyarea location of the user's body or body area.
 11. The digital method ofclaim 10, wherein the body area location comprises the user's cheek, theuser's neck, the user's head, the user's groin, the user's underarm, theuser's chest, the user's back, the user's leg, the user's arm, or theuser's bikini area.
 12. The digital method of claim 1, wherein training,by the one or more processors with the pixel data of the plurality oftraining images, the pressure model comprises training the pressuremodel to detect an amount of depression of the user's skin to determinethe user-specific pressure being applied to the user's skin by theshaving razor when the shaving stroke is taken.
 13. The digital methodof claim 1, wherein training, by the one or more processors with thepixel data of the plurality of training images, the pressure modelcomprises training the pressure model to detect an angle of a razorcartridge with respect to a razor handle of the shaving razor todetermine the user-specific pressure being applied to the user's skin bythe shaving razor when the shaving stroke is taken.
 14. The digitalmethod of claim 1, wherein training, by the one or more processors withthe pixel data of the plurality of training images, the pressure modelcomprises training the pressure model to detect an amount of depressionof the user's skin in combination with an angle of a razor cartridgewith respect to a razor handle of the shaving razor to determine theuser-specific pressure being applied the a user's skin by the shavingrazor when the shaving stroke is taken.
 15. The digital method of claim1, wherein training, by the one or more processors with the pixel dataof the plurality of training images, the pressure model comprisestraining the pressure model to detect an amount of depression of theuser's skin in combination with a direction in which a razor handle ofthe shaving razor is oriented relative to a body area of the user todetermine the user-specific pressure being applied the a user's skin bythe shaving razor when the shaving stroke is taken.
 16. The digitalmethod of claim 1, wherein training, by the one or more processors withthe pixel data of the plurality of training images, the pressure modelcomprises training the pressure model to detect an amount of depressionof the user's skin in combination with a hand grip pattern of the useron a razor handle of the shaving razor to determine the user-specificpressure being applied the a user's skin by the shaving razor when theshaving stroke is taken.
 17. The digital method of claim 1, wherein theone or more processors comprises at least one of a server or acloud-based computing platform, and the server or the cloud-basedcomputing platform receives the plurality of training images of theplurality of individuals via a computer network, and wherein the serveror the cloud-based computing platform trains the pressure model with thepixel data of the plurality of training images.
 18. The digital methodof claim 17, wherein the server or a cloud-based computing platformreceives the at least one image comprising the pixel data of at theleast a portion of the shaving razor applied to the user's skin, andwherein the server or a cloud-based computing platform executes thepressure model and generates, based on output of the pressure model, theuser-specific recommendation and transmits, via the computer network,the user-specific recommendation to the user computing device forrendering on the display screen of the user computing device.
 19. Thedigital method of claim 1, wherein the user computing device comprisesat least one of a mobile device, a tablet, a handheld device, a desktopdevice, a home assistant device, a personal assistant device, or aretail computing device.
 20. The digital method of claim 1, wherein theuser computing device receives the at least one image comprising thepixel data of the at least a portion of the shaving razor applied to theuser's skin, and wherein the user computing device executes the pressuremodel and generates, based on output of the pressure model, theuser-specific recommendation, and renders the user-specificrecommendation on the display screen of the user computing device. 21.The digital method of claim 1, wherein the at least one image comprisesa plurality of images.
 22. The digital method of claim 21, wherein theplurality of images are collected using a digital video camera.
 23. Thedigital method of claim 1, wherein the shaving razor is a wet razor, adry shaver, or an electric razor.
 24. A digital imaging systemconfigured to analyze pixel data of an image of a shaving stroke fordetermining pressure being applied to a user's skin, the digital imagingsystem comprising: an imaging server comprising a server processor and aserver memory; an imaging application (app) configured to execute on auser computing device comprising a device processor and a device memory,the imaging app communicatively coupled to the imaging server; and apressure model trained with pixel data of a plurality of training imagesof individuals and operable to determine pressure being applied to arespective individual's skin by a shaving razor when a shaving stroke istaken, wherein the pressure model is configured to execute on the serverprocessor or the device processor to cause the server processor or thedevice processor to: receive at least one image of a user when shavingwith the shaving razor, the at least one image captured by a digitalcamera, and the at least one image comprising pixel data of at least aportion of the shaving razor applied to the user's skin, analyze, by thepressure model, the at least one image captured by the digital camera todetermine a user-specific pressure being applied to the user's skin bythe shaving razor when a user shaving stroke is taken, generate, basedon the user-specific pressure, at least one user-specific electronicrecommendation designed to address at least one feature identifiablewithin the pixel data comprising the at least the portion of the shavingrazor applied to the user's skin, render, on a display screen of theuser computing device of the user, the at least one user-specificrecommendations; receive, at the one or more processors, a new image ofthe user when shaving with the shaving razor, the new image captured bythe digital camera, and the new image comprising pixel data of at leasta portion of the shaving razor applied to the user's skin; analyze, bythe pressure model executing on the one or more processors, the newimage captured by the digital camera to determine a new user-specificpressure being applied to the user's skin by the shaving razor when anew user shaving stroke is taken; generate, based on the newuser-specific pressure, a new user-specific electronic recommendation orcomment regarding at least one feature identifiable within the pixeldata of the new image; and render, on a display screen of a usercomputing device of the user, the new user-specific recommendation orcomment, wherein a delta pressure value is generated based on acomparison between the new user-specific pressure and the user-specificpressure, wherein the new user-specific recommendation or comment isfurther based on the delta pressure value, and wherein the deltapressure value, a representation of the delta pressure value, or acomment based on the delta pressure value, is rendered on the displayscreen of the user computing device.
 25. A tangible, non-transitorycomputer-readable medium storing instructions for analyzing pixel dataof an image of a shaving stroke for determining pressure being appliedto a user's skin, that when executed by one or more processors cause theone or more processors to: a. aggregate, at one or more processorscommunicatively coupled to one or more memories, a plurality of trainingimages of a plurality of individuals, each of the training imagescomprising pixel data of a respective individual when shaving with ashaving razor; b. train, by the one or more processors with the pixeldata of the plurality of training images, a pressure model operable todetermine pressure being applied to the respective individual's skin bya shaving razor when a shaving stroke is taken; c. receive, at the oneor more processors, at least one image of a user when shaving with theshaving razor, the at least one image captured by a digital camera, andthe at least one image comprising pixel data of at least a portion ofthe shaving razor applied to the user's skin; d. analyze, by thepressure model executing on the one or more processors, the at least oneimage captured by the digital camera to determine a user-specificpressure being applied to the user's skin by the shaving razor when auser shaving stroke is taken; e. generate, by the one or more processorsbased on the user-specific pressure, at least one user-specificelectronic recommendation designed to address at least one featureidentifiable within the pixel data comprising the at least the portionof the shaving razor applied to the user's skin; f. render, on a displayscreen of a user computing device, the at least one user-specificrecommendations; g. receive, at the one or more processors, a new imageof the user when shaving with the shaving razor, the new image capturedby the digital camera, and the new image comprising pixel data of atleast a portion of the shaving razor applied to the user's skin; h.analyze, by the pressure model executing on the one or more processors,the new image captured by the digital camera to determine a newuser-specific pressure being applied to the user's skin by the shavingrazor when a new user shaving stroke is taken; i. generate, based on thenew user-specific pressure, a new user-specific electronicrecommendation or comment regarding at least one feature identifiablewithin the pixel data of the new image; and j. render, on a displayscreen of a user computing device of the user, the new user-specificrecommendation or comment, wherein a delta pressure value is generatedbased on a comparison between the new user-specific pressure and theuser-specific pressure, wherein the new user-specific recommendation orcomment is further based on the delta pressure value, and wherein thedelta pressure value, a representation of the delta pressure value, or acomment based on the delta pressure value, is rendered on the displayscreen of the user computing device.